Computer chips using AI might put sniffer dogs out of work -- at least in the area of smelling dangerous chemicals in drugs, explosives, and other substances -- according to a new study published in the journal Nature. Researchers for Cornell University and Intel produced a "neuromorphic" chip called Loihi that reportedly makes computers think like biological brains, according to Daily Mail. The researchers created the circuit on the chip, mirroring organic circuits found in the olfactory bulbs of a dog's brain, which is how they process their sense of smell. The Loihi chip can identify a specific odor on the first try and even differentiate other, background smells, said Intel, according to Daily Mail. The chip can even detect smells humans emit when sick with a disease -- which varies depending on the illness -- and smells linked to environmental gases and drugs.

Of all the senses, scent is a particularly difficult one to teach AI, but that doesn't stop researchers from trying. Most recently, researchers from Intel and Cornell University trained a neuromorphic chip to learn and recognize the scents of 10 hazardous chemicals. In the future, the tech might enable "electronic noses" and robots to detect weapons, explosives, narcotics and even diseases. Using Intel's Loihi, a neuromorphic chip, the team designed an algorithm based on the brain's olfactory circuit. When you take a whiff of something, molecules stimulate olfactory cells in your nose.

In the architecture and construction industries, structural design for large buildings has always been laborious, time-consuming, and difficult to optimize. It is an iterative process that involves two steps: analyzing the current structural design by a slow and computationally expensive simulation, and then manually revising the design based on professional experience and rules. In this work, we propose an end-to-end learning pipeline to solve the size design optimization problem, which is to design the optimal cross-sections for columns and beams, given the design objectives and building code as constraints. We pre-train a graph neural network as a surrogate model to not only replace the structural simulation for speed but also use its differentiable nature to provide gradient signals to the other graph neural network for size optimization. Our results show that the pre-trained surrogate model can predict simulation results accurately, and the trained optimization model demonstrates the capability of designing convincing cross-section designs for buildings under various scenarios.

In high energy physics experiments Particle Flow (PFlow) algorithms are designed to reach optimal calorimeter reconstruction and jet energy resolution. A computer vision approach to PFlow reconstruction using deep Neural Network techniques based on Convolutional layers (cPFlow) is proposed. The algorithm is trained to learn, from calorimeter and charged particle track images, to distinguish the calorimeter energy deposits from neutral and charged particles in a non-trivial context, where the energy originated by a $\pi^{+}$ and a $\pi^{0}$ is overlapping within calorimeter clusters. The performance of the cPFlow and a traditional parametrized PFlow (pPFlow) algorithm are compared. The cPFlow provides a precise reconstruction of the neutral and charged energy in the calorimeter and therefore outperform more traditional pPFlow algorithm both, in energy response and position resolution.

Search techniques, such as Monte Carlo Tree Search (MCTS) and Proof-Number Search (PNS), are effective in playing and solving games. However, the understanding of their performance in industrial applications is still limited. We investigate MCTS and Depth-First Proof-Number (DFPN) Search, a PNS variant, in the domain of Retrosynthetic Analysis (RA). We find that DFPN's strengths, that justify its success in games, have limited value in RA, and that an enhanced MCTS variant by Segler et al. significantly outperforms DFPN. We address this disadvantage of DFPN in RA with a novel approach to combine DFPN with Heuristic Edge Initialization.

An emerging form of AI known as neuromorphic computing has been used to recognize scents emitted by explosives, chemical weapons, and narcotics. Researchers from Intel and Cornell University made the breakthrough by equipping Intel's neuromorphic test chip Loihi with neural algorithms that mimic what happens in your brain when you smell something. This enabled the system to recognize the smell of each hazardous chemical from just a single sample. The study could pave the way to a vast range of applications of neuromorphic computing, which mimics the brain's basic mechanics to make machine learning more efficient. Intel believes the "electronic nose systems" could be used by airport security to detect weapons and explosives, by police and border control to find narcotics, by robots to monitor gases pimped out into the atmosphere, and by the makers of smoke detectors to improve their products.

While the world is gearing up to fight the pandemic in the form of coronavirus, Intel has made a significant breakthrough. Intel has designed a chip that can smell various chemicals present in the air. Based on Intel's Loihi platform, the chip unsurprisingly uses machine learning algorithms to smell scents in the air. And that includes hazardous chemicals as well. The neuromorphic chip is "based on the architecture of the mammalian olfactory bulb".

Computers can already boast superhuman sensory abilities in sight and hearing, but smell has been much more difficult. The human nose isn't a particularly good one compared to the rest of the animal kingdom, but it's still a complex piece of machinery, with around 450 different types of olfactory receptors. Each of those receptor types can be activated by a range of different airborne odor molecules, each of which ping multiple different receptors at different strengths. This allows humans to distinguish between more than a trillion different scents, on top of which we can overlay a bunch of taste information to generate the sensation of flavor. Of course, it's not just how our body senses these things that's amazing – the brain's got the job of taking that huge and constantly changing swarm of electrical sensor data and processing it in real time, cross-referencing each smell signature against an impossibly massive data bank of past experiences so we can recognize it and work out whether to get hungry, or sexually aroused, or simply to wait for the next elevator.

When it comes to identifying scents, a "neuromorphic" artificial intelligence beats other AI by more than a nose. The new AI learns to recognize smells more efficiently and reliably than other algorithms. And unlike other AI, this system can keep learning new aromas without forgetting others, researchers report online March 16 in Nature Machine Intelligence. The key to the program's success is its neuromorphic structure, which resembles the neural circuitry in mammalian brains more than other AI designs. This kind of algorithm, which excels at detecting faint signals amidst background noise and continually learning on the job, could someday be used for air quality monitoring, toxic waste detection or medical diagnoses.

A standard method for testing some of the mechanical properties of materials is to poke them with a sharp point. This "indentation technique" can provide detailed measurements of how the material responds to the point's force, as a function of its penetration depth. With advances in nanotechnology during the past two decades, the indentation force can be measured to a resolution on the order of one-billionth of a Newton (a measure of the force approximately equivalent to the force you feel when you hold a medium-sized apple in your hand), and the sharp tip's penetration depth can be captured to a resolution as small as a nanometer, or about 1/100,000 the diameter of a human hair. Such instrumented nanoindentation tools have provided new opportunities for probing physical properties in a wide variety of materials, including metals and alloys, plastics, ceramics, and semiconductors. But while indentation techniques, including nanoindentation, work well for measuring some properties, they exhibit large errors when probing plastic properties of materials -- the kind of permanent deformation that happens, for example, if you press your thumb into a piece of silly putty and leave a dent, or when you permanently bend a paper clip using your fingers.